Traffic detection with multiple outputs depending on type of object detected

ABSTRACT

A multi-object zonal traffic detection framework analyzes temporal and spatial information from one or more sensors in a classification engine that identifies and differentiates objects within a single identified traffic detection zone. The classification engine applies a whole scene analysis and an adaptive background zone detection model to classify cars, trucks, bicycles, pedestrians, incidents, and other objects within the single identified traffic detection zone and generates counts for each object type for traffic system management.

FIELD OF THE INVENTION

The present invention relates to identification of objects in the fieldof traffic detection. Specifically, the present invention relates to asystem and method of classifying multiple objects within a singletraffic detection zone and generating different outputs for a trafficsignal controller.

BACKGROUND OF THE INVENTION

There are many conventional traffic detection systems. Conventionalsystems typically utilize sensors, either in the roadway itself, orpositioned at a roadside location or on traffic lights proximate to theroadway. The most common type of vehicular sensors are inductive coils,or loops, embedded in a road surface. Other existing systems utilizevideo cameras, radar sensors, acoustic sensors, or magnetometers, eitherin the road itself, or at either the side of a roadway or positionedhigher above traffic to observe and detect vehicles in a desired area.Each of these sensors provide information used to determine a presenceof vehicles in specific lanes in intersections, to provide informationto traffic signals for proper actuation.

These conventional detection systems are commonly set up with ‘virtualzones’, which are hand- or machine-drawn areas on an image where objectsmay be moving or present. Traditionally, a vehicle passes through orstops in a zone, and these zones generate an “output” when an object isdetected as passing through or resting within all or part of the zone.

Many detection systems are capable of detecting different types ofvehicles, such as cars, trucks, bicycles, motorcycles, pedestrians, etc.This is accomplished by creating special zones within a field of view todifferentiate objects, such as bicycle zones and pedestrian zones.Therefore, conventional detection systems are capable ofdifferentiating, for example, bicycles from other types of vehicles byanalyzing these special zones. However, one limitation of this approachis that multiple zones have to be drawn, often over the top of eachother at the same location, to be able to provide outputs for differentmodes. Therefore there is a need in the art for a system and methodwhich is capable of differentiating between objects in only one zonewithin an area of traffic detection.

Outputs are sent to a traffic signal controller, which performs controland timing functions based on the information provided. These outputsalso provide traffic planners and engineers with information on thevolume of traffic at key points in a traffic network. This informationis important for comparing volumes over periods of time to help withaccurate adjustment of signal timing and managing traffic flow. Currentsystems and methods of traffic detection provide data that results onlyfrom a count of a total number of vehicles, which may or may not includebicycles or other road users, as therefore there is no waydifferentiating between different types of vehicles. As the need formodified signal timing to accommodate bicyclists, pedestrians and othersbecomes more critical for proper traffic management, a method forseparating the count of all modes of use on a thoroughfare is needed toimprove the ability to accurately manage traffic environments.

BRIEF SUMMARY OF THE INVENTION

It is therefore one objective of the present invention to provide asystem and method of identifying multiple objects in a single trafficdetection zone. It is another objective of the present invention toprovide a system and method of accurately classifying objects within anidentified traffic detection using data from different types of sensors.It is still another objective to provide separate counts for differenttypes of objects within a traffic detection zone to traffic signalcontrollers.

The present invention provides systems and methods of identifying anarea of interest in a field of view, otherwise referred to as a trafficdetection zone, and generating multiple outputs based on the type ofobject detected within that traffic detection zone. These systems andmethods present a multi-object zonal traffic detection framework that isinitialized by identifying an area of interest and drawing a singletraffic detection zone for that area of interest in the field of view.The traffic detection zone is configured to provide separate outputs andcounts that depend on the type of object detected. Several possibleoutputs are initialized, for example:

-   -   Output A for Commercial Vehicles, Large Trucks    -   Output B for Commercial Vehicles, Cars, Light Trucks    -   Output C for Bicycles, Motorcycles    -   Output D for Pedestrians    -   Output E for Incidents        It should be noted that many other outputs are possible, and the        present invention can be initialized with any number outputs and        object types, and therefore is not to be limited to any specific        number.

In addition, the traffic detection zone is configured to produce a countof each type of object detected. Using the geometry of the zone drawn, alane structure of a particular traffic approach can be estimated andindividual zone counts can be aggregated into lane-wise counts ofdifferent object types. This output is stored locally for laterretrieval or transmission to a central system for analysis andpresentation.

The multi-object zonal traffic detection framework includes aclassification engine that constantly learns as more information iscollected and ingested from the sensor systems. The engine classifiesdifferent objects into cars, trucks, bicycles, pedestrian, incidents,and others based on the unique features of each class, and continuouslyand adaptively updates its knowledge of unique features of each class asmore objects are processed.

Other objects, embodiments, features and advantages of the presentinvention will become apparent from the following description of theembodiments, taken together with the accompanying drawings, whichillustrate, by way of example, the principles of the invention.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate several embodiments of theinvention and together with the description, serve to explain theprinciples of the invention.

FIG. 1 is a block diagram illustrating an object entering a trafficdetection zone and various outputs following classification according tothe multi-object zonal traffic framework of the present invention;

FIG. 2 is a flowchart of steps in a multi-object zonal traffic detectionframework according to one aspect of the present invention;

FIG. 3 is a flowchart of steps performed in a whole scene analysisaccording to one aspect of the present invention;

FIG. 4 is a flowchart of steps performed in a background detection andlearning analysis according to one aspect of the present invention;

FIG. 5 is a flowchart of steps performed in an object classificationanalysis according to one aspect of the present invention; and

FIG. 6 is a system diagram for a multi-object zonal traffic detectionframework and classification engine according to one aspect of thepresent invention.

DETAILED DESCRIPTION OF THE INVENTION

In the following description of the present invention reference is madeto the exemplary embodiments illustrating the principles of the presentinvention and how it is practiced. Other embodiments will be utilized topractice the present invention and structural and functional changeswill be made thereto without departing from the scope of the presentinvention.

FIG. 1 and FIG. 2 are a block diagram and a flowchart, respectively,outlining basic elements of the multi-object zonal traffic detectionframework 100. FIG. 1 shows an object 101 entering an identified trafficdetection zone 103. The framework 100 is configured to analyze the zone103, classify the object 101 according to an object type 102, andgenerate an output 104. A different output 104 is generated for eachobject 101 that is classified as a type 102—for example, differentoutputs 104 for a car 105, a bicycle 106, a truck or other commercialvehicle 107, a pedestrian 108, or an incident 109.

It should be noted that many additional object types 102 are possibleand may be configured within the framework 100, and therefore are withinthe scope of the present invention. For example, additional outputs 104may be configured for disabled persons using wheelchairs or motorizedforms of transportation similar to wheelchairs, disabled persons usingthe aid of guide animals, and for elderly pedestrians moving slowerand/or with walking aids. Accordingly the present invention is notintended to be limited by any listing of outputs herein.

FIG. 2 is a flowchart of steps in a process of performing a multi-objectzonal traffic detection framework 100 for evaluating one or more objects101 within the identified traffic detection zone 103, and generatingoutputs 104, according to one embodiment of the present invention. Theframework 100, as noted above, includes a classification engineperformed within one or more systems and/or methods that includesseveral components, each of which define distinct activities required toclassify an object 101 in the traffic detection zone 103, to generateone or more output signals 110 for use with traffic signal controllers196, and to enable a tool 170 configured to manage system functions.

Referring to FIG. 2, the present invention is initialized by the step120 of identifying and drawing a traffic detection zone 103. The trafficdetection zone 103 may be initialized by a user via the trafficmanagement tool 170, for example via an application resident on acomputing device and/or using a graphical user interface. The user atstep 120 may select a specific size and location of a traffic detectionzone 103 in relation to a traffic intersection or other portion of aroadway, using the traffic management tool 170. The traffic detectionzone 103 may therefore be pre-selected by a user prior to performance ofthe classification engine defined herein, and may also be adjusted bythe user during system performance. Alternatively, the size, location,and number of traffic detection zones 103 may be automatically selectedand adjusted.

The different outputs 104 for object types 102 are assigned at step 122,and the framework 100 then commences system operation and datacollection at step 124. At step 126, the present invention analyzes andprocesses input data 130 from one or more sensors 132 and generates 128one or more output signals 110 based on the object type 102.

Input data 130 is collected from the one or more sensors 132, which maybe positioned in or near a roadway area for which the traffic detectionzone 103 is identified and drawn. The one or more sensors 132 includevideo systems 133 such as cameras, thermal cameras, radar systems 134,magnetometers 135, acoustic sensors 136, and any other devices orsystems 137 which are capable of detecting a presence of objects withina traffic environment.

FIG. 3 is a flow diagram showing steps in a whole scene analysis 140,which may be performed, along with other elements of the presentinvention, within a computing environment 180 that is configured atleast in part to execute one or more program instructions in a pluralityof data processing modules 181 that includes a specific whole sceneanalysis module 183 for performing whole scene analysis 140. Thecomputing environment 180 also includes one or more processors 189 and aplurality of software and hardware components as well as memorycomponents for storing and recalling the one or more programinstructions. The one or more processors 189 and plurality of softwareand hardware components are configured to perform the functions of themulti-object zonal traffic detection framework 100 described herein, andembodied in the one or more data processing modules 181. The whole sceneanalysis 140 and whole scene analysis module 183 process temporalinformation 138 in the input data 130 by examining a complete “field ofview” in the data provided by the one or more sensors 132.

This whole scene analysis 140 associates data points, such as forexample pixels, using common data point characteristics 143 and attemptsto identify 141 one or more groups of moving data points 142. In oneaspect of the whole scene analysis 140, if it is determined, at step144, that an association of moving data points is a new group 142, anumber is assigned to the group 142, and group attributes 145 aredefined. If the group 142 is not new, group attributes 145 are updatedat step 146, and a preliminary identification of a group class, orobject type 102, is made at step 147. An output 148 from this wholescene analysis 140 is provided to a zone detection zone backgroundlearning module 184 and a classification module 186.

The whole scene analysis 140 analyzes the temporal information 138 byexamining every data point in the sensor data to, as noted above,associate groups of moving data points 142 that have common data pointcharacteristics 143 (not shown). The common characteristics 143 enablean initial identification of a group of moving data points 142 as aforeground object 149. The common characteristics 143 examined in thiswhole scene analysis 140 at least include a color, a luminance, aposition, and movement of the data points to identify an object inmotion. The whole scene analysis 140 tries to identify groups of movingdata points 142 by looking for commonalties in these characteristics 143to arrive at a conclusion that the object 101 is a foreground object149.

FIG. 4 is a flow diagram showing steps in a detection zone backgroundlearning model 150, which may be performed by a specific module 184within the computing environment 180. The detection zone backgroundlearning model 150 and module 184 process spatial information 139 in theinput data 130 and is initialized with information defining the trafficdetection zone 103 and the input data 130.

The detection zone background learning model 150 examines specific datapoint attributes 151 within the identified traffic detection zone 103,and attempts to adaptively learn what is in the background 155 (notshown) over time. The classification engine applies this learned model150 to differentiate all of or a portion of detection zone data pointsfrom known background objects 157.

At step 152, the model 150 extracts multi-dimensional spatial features154, and then learns statistical thresholds for backgroundcharacteristics 153 (not shown) at step 156. This results in an adaptivemodel of the background 155 of the identified traffic detection zone 103that is continuously generated and adjusted as additional input data 130is ingested into the multi-object zonal traffic detection framework 100.Through this process, the present invention continually learns whatobjects are part of the background 155 for subsequent differentiation inthe classification module 186.

Background characteristics 153 include one or more of a roadway surface,roadway or lane markings, and roadway shadows within the identifiedtraffic detection zone 103. These may include permanent and temporarycharacteristics as well as items which change over time at differentrates. For example, other background characteristics 153 may includetemporary items such as road markers or traffic cones which are placedfor an extended or particular period of time within the identifiedtraffic detection zone 103. Also, a roadway surface may include asurface texture, permanent markings or fixtures, tree shadows, andbuilding shadows which may have only minimal or slow changes over acertain period of time. The detection zone background learning model 150looks at specific multi-dimensional data point attributes 151 in theinput data 130 collected by the one or more sensors 132 to identifybackground characteristics 153 and learn what may be part of thebackground 155. Examples of these multi-dimensional data pointattributes 151 include a pixel histogram, directional edges, and a grayscale mean. Other examples include a motion analysis (optical flow),frame difference data, and corner features.

In one embodiment of the present invention, the detection zonebackground learning model 150 may also be configured to monitor datapoints over time to determine if they represent a part of thebackground. For example, if data points such as pixels are present forless than specified period of time, the algorithm determines that theyrepresent a foreground object 149. If the pixels are present for greaterthan a specified period of time, the algorithm determines that theyrepresent a part of the background 155. The traffic management tool 170may include the capability to allow the user to set these specifiedperiods of time.

Together, the whole scene analysis 140 and the detection zone backgroundlearning model 150 enable a preliminary distinction between foregroundobjects 149 and background objects 157. Using the whole scene analysis140, the present invention tracks a moving object 101 and knows that themoving object 101 has entered the identified traffic detection zone 103.This helps the detection zone background learning model 150 to discernthat changes in the background 155 of the identified traffic detectionzone 103 are caused by the intrusion of the object 101, thus matchingthe background change to the actual moving object 101 and enabling adifferentiation between foreground objects 149 and background objects157.

The classification module 186 of the multi-object zonal trafficdetection framework 100 performs the steps shown in FIG. 5 in aclassification analysis 160. This module 186 is initialized with outputfrom the temporal analysis of the whole scene analysis 140, and withoutput from the spatial analysis of the detection zone backgroundlearning model 150. The classification module 186 applies these outputsto classify the temporal information 138 and spatial information 139 bydetermining, at step 161, if a group of moving data points 142 in theforeground of the identified traffic detection zone 103 represent one ormore foreground objects 149, and if so, identifying, at step 166, anobject type 102 based on dominant object type features for each objecttype 102.

A group of moving data points 142 is determined to be a foregroundobject 149 by applying the preliminary identification of a foregroundobject 149 at step of 147 of the whole scene analysis 140 and what is inthe known background 155 from the detection zone background learningmodel 150. If the foreground object 149 does not form a part of theknown background 155, the classification analysis 160 proceeds withattempting to classify the foreground object 149 as one of severalobject types 102, including a motorized passenger vehicle or car, alarger commercial vehicle or truck, a two-wheeled vehicle such as abicycle or a motorcycle, a pedestrian or group of pedestrians, and anincident.

Object type features are used to identify an object by applying severaldifferent analyses to data points in the temporal information 138 andthe spatial information 139. In the case of pixels as data points, theseinclude a pixel texture analysis 162, a pixel intensity analysis 163, apixel shape analysis 164, and a pixel edge analysis 165. Differentsub-modules may be utilized to perform these analyses. These differentsub-modules analyze, respectively, pixel characteristics such as pixeltexture content, pixel intensity, pixel shape, and pixel edges, as wellas object attributes of groups of moving pixels 142 that include width,height, contour, centroid, and moments, and object tracking attributesof groups of moving pixels 142, such as speed, velocity, number offrames observed, number of frames missed, and object trajectory.

Continuing with the example of pixels as data points, once the analysesabove are applied, the classification analysis 160 and classificationmodule 186 proceed with applying specific dominant object type featuresthat are known for each object type 102 and comparing with the pixeltexture content, pixel intensity, pixel shape, pixel edges, objectattributes, and object tracking attributes to assign an object type 102to each group of moving pixels 142 that has been determined to be aforeground object 149.

It should be noted that the following classifications are exemplaryclassifications of objects 101, and may be changed or adjusted by a userof the present invention. Where the pixel/data point analysis in theclassification module 186 indicates, for example, a medium height, lowwidth, sparsely distributed pixel intensities, low number of edges, andlow-to-medium speed, the object 101 may be classified as a bicycle 106,and an appropriate output 104 is generated for a traffic managementsystem 194 and to the counting module 188. Where the pixel/data pointanalysis of the classification module 186 indicates, for example, amedium-to-large height, medium-to-large width, pixel intensitiesconcentrated over a narrow range, medium-to-high number of edges, andmedium-to-high speed, the object 101 may be classified as a motorizedpassenger vehicle or car 105, and an appropriate output 104 is generatedfor a traffic management system 194 and to the counting module 188.

Where the pixel/data point analysis in the classification module 186indicates, for example, a large height, a large width, pixel intensitiesdistributed over a few bands, high number of edges, and a medium speed,the object 101 may be classified as a truck or large commercial vehicle107, and an appropriate output 104 is generated for a traffic managementsystem 194 and to the counting module 188. It should be noted that suchtrucks can typically span more than one detection zone 103, andtherefore the classification analysis 160 may combine features from oneor more of neighboring traffic detection zones to make a finalclassification of an object 101 as a truck 107.

Where the pixel/data point analysis in the classification module 186indicates, for example, a particular direction of the movement, speed ofthe movement, shape, certain body signature, and special body pose, theobject 101 may be classified as a pedestrian 108, and an appropriateoutput 104 is generated for a traffic management system 194 and to thecounting module 188. In the case of incidents 109, the present inventionlooks for pixel characteristics 143 and attributes 151 that can besegmented over a specified period of time, so that it is more likelyindicative of debris in zone, stopped vehicles, wrong-way traffic etc.to generate an appropriate output 104 for a traffic management system194 and the counting module 188.

The classification analysis 160 may also apply a statistical classifier168 to further analyze multi-dimensional object attributes anddifferentiate objects 101 of multiple types 102. Such a statisticalclassifier 168 may be either a supervised classifier or an unsupervisedclassifier, or a combination of both. Examples of supervised classifiersinclude SVM, CNN/deep learning, etc., and examples of unsupervisedclassifiers include K-means clustering, expectation maximization, GMM,etc. Where a supervised classifier is incorporated into theclassification analysis 160 in the present invention, the statisticalclassifier may be trained through many training samples (such as carsamples, bicycle/motorcycle samples, pedestrian samples, truck samplesand samples of different kinds of background. After the supervisedclassifier is trained, it is able to identify different objects 101 fromnewly input images as cars, bicycles, pedestrians, etc. based on whathas been learned from the training.

FIG. 6 is a system diagram for a multi-object zonal traffic detectionframework 100 of the present invention. Input data 130, comprised ofinformation from the one or more sensors 132, is ingested via a dataingest module 182 for processing within a classification engine,comprised at least of the whole scene analysis 140, the zone detectionbackground learning model 150, and the classification analysis 160.

The data ingest module 182 provides the input data 130 to the wholescene analysis module 183, which processes temporal information 138 fromthe input data 130 collected by the sensors 132 and performs the stepsas in FIG. 3. The data ingest module 182 also provides the input data130 to the detection zone background learning module 184, whichprocesses spatial information 139 from the input data 130 collected bythe sensors 132 and performs the steps as in FIG. 4.

The plurality of data processing modules 181 within the framework 100may also include a traffic detection zone initialization module 187,which is responsible for drawing and identifying a traffic detectionzone 103 as an initialization of the framework 100 in step 120 of FIG.2. A user may manually identify and draw a traffic detection zone 103using a traffic management tool 170, or a traffic detection zone 103 maybe identified and drawn automatically. Regardless, information about theidentified traffic detection zone 103 is provided to the data ingestmodule 182 for distribution to the classification engine for analyzingthe temporal information 138 and the spatial information 139.

The multi-object zonal traffic detection framework 100 may also includea counting module 188, which performs and maintains a count 190 (notshown) of different object types 102. Object types 102 are assigned asin FIG. 2 by the initialization module 187, and outputs 104 representingdifferent object types 102—car 105, bicycle 106, truck or othercommercial vehicle 107, pedestrian 108, and incident 109—are provided tothe counting module 188. The traffic detection zone 103 and framework100 therefore generate a count 190 of each object type 102 detected bythe classification engine. Using the geometry of all the trafficdetection zones 103 that are drawn, the lane structure of a particulartraffic approach can be estimated and individual zone counts can beaggregated into lane-wise counts of different object types 102. Thisoutput 104 is stored locally for later retrieval or transmission to acentralized traffic management system 194 for analysis and presentationusing the traffic management tool 170.

The counting module 188 increments a count 190 for an object type 102each time a particular object 101 leaves the identified trafficdetection zone 103. This count 190 may be stored temporarily in localmemory within the computing environment 180. The user may configure a‘Bin Interval’ of one of a plurality of time bases. The framework 100monitors this time base, and once a Bin Interval expires, the counts arestored in a database for later retrieval. Such a process is repeatedcontinually.

Retrieval and viewing of the counts 190 may be performed by multiplemethods. One such method is local viewing on using an on-screen displayvia a graphical user interface or the like. Counts 190 may also beremotely retrieved and viewing using the traffic management tool 170directly, or using a computer-based platform or application, such as forexample on a desktop, laptop, or tablet computing device or mobiletelephony device. Counts 190 may also be accessed automatically througha remote system that interrogates nightly and downloads count data to alocal database for viewing, creating of charts, graphs and reports.

The traffic management tool 170 supports both zone and lane analytics192, and a traffic management system 194 for control of a traffic signalcontroller 196 using the output data 110. Zone and lane analytics 192use output from the counting module 188. The traffic management supporttool 170 may include widgets, drop-down menus, and other indiciapresented via a graphical user interface that enable a user to makeselections and perform functions attendant to operation of themulti-object zonal traffic detection framework 100.

The systems and methods of the present invention may be implemented inmany different computing environments. For example, they may beimplemented in conjunction with a special purpose computer, a programmedmicroprocessor or microcontroller and peripheral integrated circuitelement(s), an ASIC or other integrated circuit, a digital signalprocessor, electronic or logic circuitry such as discrete elementcircuit, a programmable logic device or gate array such as a PLD, PLA,FPGA, PAL, and any comparable means. In general, any means ofimplementing the methodology illustrated herein can be used to implementthe various aspects of the present invention. Exemplary hardware thatcan be used for the present invention includes computers, handhelddevices, telephones (e.g., cellular, Internet enabled, digital, analog,hybrids, and others), and other such hardware. Some of these devicesinclude processors (e.g., a single or multiple microprocessors orgeneral processing units), memory, nonvolatile storage, input devices,and output devices. Furthermore, alternative software implementationsincluding, but not limited to, distributed processing, parallelprocessing, or virtual machine processing can also be configured toperform the methods described herein.

The systems and methods of the present invention may also be partiallyimplemented in software that can be stored on a storage medium, executedon programmed general-purpose computer with the cooperation of acontroller and memory, a special purpose computer, a microprocessor, orthe like. In these instances, the systems and methods of this inventioncan be implemented as a program embedded on a mobile device or personalcomputer through such mediums as an applet, JAVA® or CGI script, as aresource residing on one or more servers or computer workstations, as aroutine embedded in a dedicated measurement system, system component, orthe like. The system can also be implemented by physically incorporatingthe system and/or method into a software and/or hardware system.

Additionally, the data processing functions disclosed herein may beperformed by one or more program instructions stored in or executed bysuch memory, and further may be performed by one or more modulesconfigured to carry out those program instructions. Modules are intendedto refer to any known or later developed hardware, software, firmware,artificial intelligence, fuzzy logic, expert system or combination ofhardware and software that is capable of performing the data processingfunctionality described herein.

The foregoing descriptions of embodiments of the present invention havebeen presented for the purposes of illustration and description. It isnot intended to be exhaustive or to limit the invention to the preciseforms disclosed. Accordingly, many alterations, modifications andvariations are possible in light of the above teachings, may be made bythose having ordinary skill in the art without departing from the spiritand scope of the invention. It is therefore intended that the scope ofthe invention be limited not by this detailed description. For example,notwithstanding the fact that the elements of a claim are set forthbelow in a certain combination, it must be expressly understood that theinvention includes other combinations of fewer, more or differentelements, which are disclosed in above even when not initially claimedin such combinations.

The words used in this specification to describe the invention and itsvarious embodiments are to be understood not only in the sense of theircommonly defined meanings, but to include by special definition in thisspecification structure, material or acts beyond the scope of thecommonly defined meanings. Thus if an element can be understood in thecontext of this specification as including more than one meaning, thenits use in a claim must be understood as being generic to all possiblemeanings supported by the specification and by the word itself.

The definitions of the words or elements of the following claims are,therefore, defined in this specification to include not only thecombination of elements which are literally set forth, but allequivalent structure, material or acts for performing substantially thesame function in substantially the same way to obtain substantially thesame result. In this sense it is therefore contemplated that anequivalent substitution of two or more elements may be made for any oneof the elements in the claims below or that a single element may besubstituted for two or more elements in a claim. Although elements maybe described above as acting in certain combinations and even initiallyclaimed as such, it is to be expressly understood that one or moreelements from a claimed combination can in some cases be excised fromthe combination and that the claimed combination may be directed to asub-combination or variation of a sub-combination.

Insubstantial changes from the claimed subject matter as viewed by aperson with ordinary skill in the art, now known or later devised, areexpressly contemplated as being equivalently within the scope of theclaims. Therefore, obvious substitutions now or later known to one withordinary skill in the art are defined to be within the scope of thedefined elements.

The claims are thus to be understood to include what is specificallyillustrated and described above, what is conceptually equivalent, whatcan be obviously substituted and also what essentially incorporates theessential idea of the invention.

The invention claimed is:
 1. A method comprising: ingesting input datathat includes sensor data collected for an area of interest at a trafficintersection; modeling the input data within a computing environment ina plurality of data processing modules executed in conjunction with atleast one specifically-configured processor, the data processing modulesconfigured to detect and classify multiple moving objects in the area ofinterest at the traffic intersection traffic detection zone, by drawinga traffic detection zone comprised of different zones used by themultiple moving objects in the area of interest; analyzing temporalinformation by examining pixels in the sensor data to associate groupsof moving pixels having common pixel characteristics and initiallyidentify a group of moving pixels as a foreground object in thedifferent zones; analyzing spatial information by extracting andexamining specific multi-dimensional pixel attributes in the sensor datathat include one or more of a pixel histogram, directional edges, andgray scale mean to identify background characteristics and estimate lanestructures and other roadway markings within the traffic detection zone,to adaptively learn a background information model of the estimated lanestructures and other roadway markings that identify zones for vehicular,cyclist, and pedestrian use among the different zones within the area ofinterest over time; applying the background information model todetermine if one or more of the pixels in the area of interest conformto the background characteristics and estimated lane structures andother roadway markings; and classifying the temporal information andspatial information by determining if a group of moving pixels representone or more foreground objects inside the zones for vehicular, cyclist,and pedestrian use, and identifying an object type of the one or moreforeground objects based on dominant object type features that includeone or more of pixel intensity, pixel edges, pixel texture content,pixel shape, object attributes, and object tracking attributes for eachobject type, wherein the object type is at least one of a bicycle, atruck, a passenger vehicle, a pedestrian, or an incident; andgenerating, as output data, information representing the object type toa traffic controller system.
 2. The method of claim 1, wherein thecommon pixel characteristics in the analyzing temporal information inthe sensor data at least include color, luminance, position, andmovement.
 3. The method of claim 1, wherein the backgroundcharacteristics include one or more of a roadway surface and roadwayshadows within the identified traffic detection zone.
 4. The method ofclaim 1, wherein the analyzing spatial information further comprisesmonitoring pixels over time to determine if they represent a part of thebackground, wherein if the pixels are present for less than specifiedperiod of time, they represent a foreground object, and if the pixelsare present for greater than a specified period of time, they representa part of the background.
 5. The method of claim 1, wherein the objectattributes include at least one of width, height, contour, centroid, andmoments, and the object tracking attributes include at least one ofspeed, velocity, number of frames observed, number of frames missed, andobject trajectory.
 6. The method of claim 1, further comprisinginitiating a count of each object type present in the zones forvehicular, cyclist, and pedestrian use.
 7. The method of claim 6,further comprising estimating a lane structure for the zones forvehicular, cyclist, and pedestrian use and initiating a count of eachobject type for each lane.
 8. The method of claim 1, further comprisingtraining a statistical classifier to further examine the specificmulti-dimensional pixel attributes to classify the temporal and spatialinformation, so that different foreground objects are identified asadditional input data is ingested.
 9. The method of claim 1, wherein thesensor data is captured by at least one of a video camera, a radarsystem, and a magnetometer.
 10. The method of claim 1, furthercomprising selecting a size and location of a traffic detection zone andthe different zones used by the multiple moving objects in the area ofinterest using a traffic management tool.
 11. A method of detectingmultiple moving objects in a traffic intersection, comprising: drawing atraffic detection zone comprised of different zones within an area ofinterest at a traffic intersection; associating one or more pixelshaving common pixel characteristics in collected sensor data for thearea of interest to initially identify a group of moving pixels in atemporal whole scene analysis of the different zones, the common pixelcharacteristics at least including pixel color, pixel luminance, pixelposition, and pixel movement; developing a spatial background model toadaptively learn a background of estimated lane structures and otherroadway markings that identify zones for vehicular, cyclist, andpedestrian use among the different zones within the area of interestover time in a zonal background analysis of the area of interest, byextracting and examining specific multi-dimensional pixel attributes inthe sensor data to identify background characteristics and estimate thelane structures and other roadway markings defining the zones forvehicular, cyclist, and pedestrian use among the different zones withinthe traffic detection zone, and differentiate whether all or a portionof the pixels in the area of interest pixels conform to the backgroundbased on the identified background characteristics and estimated lanestructures and other roadway markings; determining if a group of movingpixels represents a foreground object inside the zones for vehicular,cyclist, and pedestrian use among the different zones; anddifferentiating one or more foreground objects to identify an objecttype as either a bicycle, a passenger vehicle, a truck, a pedestrian, oran incident based on 1) evaluating a plurality of dominant object typefeatures that include one or more of pixel intensity, pixel edges, pixeltexture content, pixel shape, object attributes, and object trackingattributes, and 2) training a statistical classifier to further identifythe object type from the dominant object type features in additionalinput data that is collected from one or more sensors.
 12. The method ofclaim 11, further comprising generating an output representing theobject type to a traffic controller system.
 13. The method of claim 11,wherein the background characteristics include one or more of a roadwaysurface and roadway shadows within the identified traffic detectionzone.
 14. The method of claim 11, further comprising monitoring pixelsover time to determine if they represent a part of the background,wherein if the pixels are present for less than specified period oftime, they represent a foreground object, and if the pixels are presentfor greater than a specified period of time, they represent a part ofthe background.
 15. The method of claim 11, wherein the objectattributes include at least one of width, height, contour, centroid, andmoments, and the object tracking attributes include at least one ofspeed, velocity, number of frames observed, number of frames missed, andobject trajectory.
 16. The method of claim 11, further comprisinginitiating a count of each object type present in the zones forvehicular, cyclist, and pedestrian use.
 17. The method of claim 16,further comprising estimating a lane structure for the zones forvehicular, cyclist, and pedestrian use and initiating a count of eachobject type for each lane.
 18. The method of claim 11, wherein the oneor more sensors include least one of a video camera, a radar system, anda magnetometer.
 19. The method of claim 11, further comprising selectinga size and location of a traffic detection zone and the different zonesin the area of interest using a traffic management tool.
 20. A system,comprising: a computing environment including at least onenon-transitory computer-readable storage medium having programinstructions stored therein and a computer processor operable to executethe program instructions within a plurality of data processing modulesto detect multiple moving objects within an area of interest at atraffic intersection, the plurality of data processing modulesincluding: a whole scene analysis module configured to draw a trafficdetection zone comprised of different zones used by the multiple movingobjects in the area of interest, and analyze temporal information ininput data collected by one or more sensors for the area of interest byexamining pixels inside the area of interest to associate groups ofmoving pixels having common pixel characteristics and initiallyidentifying one or more groups of moving pixels as foreground objects inthe different zones based on the common pixel characteristics; adetection zone background learning module configured to 1) analyzespatial information in the input data collected by one or more sensorsfor the area of interest by examining specific multi-dimensional pixelattributes inside the identified traffic detection zone, 2) identifybackground characteristics to adaptively learn a background informationmodel of estimated lane structures and other roadway markings thatidentify zones for vehicular, cyclist, and pedestrian use among thedifferent zones within the area of interest over time, and 3) apply thebackground information model to determine if one or more of the pixelsin the area of interest conform to the background characteristics andestimated lane structures and other roadway markings; a classificationmodule configured to classify the analyzed temporal information and theanalyzed spatial information by 1) determining if a group of movingpixels represents one or more foreground objects inside the zones forvehicular, cyclist, and pedestrian use, and 2) identifying an objecttype of the one or more foreground objects based on dominant object typefeatures that include one or more of pixel intensity, pixel edges, pixeltexture content, pixel shape, object attributes, and object trackingattributes for each object type; and an output module configured tocommunicate the object type to a traffic controller system.
 21. Thesystem of claim 20, wherein the object type is at least one of abicycle, a truck, a passenger vehicle, a pedestrian, or an incident. 22.The system of claim 20, wherein the common pixel characteristics in theanalyzing temporal information in the sensor data at least includecolor, luminance, position, and movement.
 23. The system of claim 20,wherein the background characteristics include one or more of a roadwaysurface and roadway shadows within the identified traffic detectionzone.
 24. The system of claim 20, wherein the detection zone backgroundlearning module is further configured to monitor pixels over time todetermine if they represent a part of the background, wherein if thepixels are present for less than specified period of time, theyrepresent a foreground object, and if the pixels are present for greaterthan a specified period of time, they represent a part of thebackground.
 25. The system of claim 20, wherein the object attributesinclude at least one of width, height, contour, centroid, and moments,and the object tracking attributes include at least one of speed,velocity, number of frames observed, number of frames missed, and objecttrajectory.
 26. The system of claim 20, further comprising an objectcounting module that initiates a count of each object type present inthe zones for vehicular, cyclist, and pedestrian use.
 27. The system ofclaim 26, wherein a lane structure within the zones for vehicular,cyclist, and pedestrian use is estimated, and the object counting moduleinitiates a count of each object type for each lane.
 28. The system ofclaim 20, wherein the classification module is further configured toclassify the analyzed temporal information and the analyzed spatialinformation by training a statistical classifier to further identify theobject type from the dominant object type features in additional inputdata that is collected from the one or more sensors.
 29. The system ofclaim 20, wherein the one or more sensors include least one of a videocamera, a radar system, and a magnetometer.
 30. The system of claim 20,further comprising a traffic management tool configured to enable a userto identify a size and location of the traffic detection zone and thedifferent zones used by the multiple moving objects in the area ofinterest.